Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

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Reinforcement learning (RL) holds great promise for enabling robots to learn complex manipulation skills, but realizing this potential in real-world settings has been challenging. This paper presents a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic manipulation, precision assembly, and dual-arm coordination.

The system, named Human-in-the-Loop Sample-Efficient Robotic Reinforcement Learning (HIL-SERL), addresses common challenges in real-world RL by integrating human demonstrations and corrections, efficient RL algorithms, and other system-level design choices.

Key Features of HIL-SERL:

• Human-in-the-Loop Corrections: The system allows human operators to intervene and provide corrective actions during training. This helps the policy learn from its mistakes and improve performance, particularly for challenging tasks.
• Pretrained Vision Backbones: HIL-SERL utilizes pretrained vision backbones to process image data, making the learning process more efficient and robust.
• Sample-Efficient Off-Policy RL: The system utilizes a sample-efficient off-policy RL algorithm based on RLPD (Ball et al., 2023) that also incorporates human demonstrations and corrections.
• Modular Environment: The environment supports multiple cameras, integration of input devices, and control of multiple robotic arms, making the system flexible and adaptable.
• Sparse Reward Function: The reward function uses a trained classifier to determine whether a task is successful or not, providing a simple and effective way to guide the learning process.

Experiment Results:

HIL-SERL achieves near-perfect success rates and super-human cycle times on a wide range of tasks, including:

• Dynamic Manipulation: Flipping an object in a pan, whipping out a Jenga block from its tower.
• Precise Manipulation: Inserting a RAM card into its slot, assembling a PCI-E SSD into a motherboard, and securing a USB cable into a tight-fitting clip.
• Dual-Arm Coordination: Assembling a car dashboard, performing a coordinated handover task, and assembling a timing belt.
• Flexible Object Manipulation: Assembling an IKEA shelf.

The results demonstrate that HIL-SERL significantly outperforms imitation learning methods that are trained on the same amount of human data. In particular, the trained RL policies achieve an average 2x improvement in success rate and 1.8x faster execution speed compared to the imitation learning baseline.

Robustness and Generalization:

The paper also demonstrates the robustness and generalization of HIL-SERL through qualitative evaluations. The system is able to adapt to external disturbances and variations in the task environment, such as moving objects and unexpected deformations during execution.

Implications:

HIL-SERL offers a promising approach to training robots to perform a wide range of complex manipulation tasks in the real world. The system’s impressive performance, robustness, and generalization capabilities suggest that it could be a valuable tool for developing a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements.

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